Subjects

Individual runs

First run

run1_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run1_data)
##            term   estimate std.error  statistic       p.value
## 1   (Intercept) -2.3535356 0.0739461 -31.827717 2.677800e-222
## 2        mask_c  0.1141501 0.1002212   1.138981  2.547110e-01
## 3        feat_c  0.3238524 0.1002257   3.231232  1.232577e-03
## 4 mask_c:feat_c  0.4674783 0.2004747   2.331856  1.970826e-02

Second run

run2_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run2_data)
##            term    estimate  std.error   statistic       p.value
## 1   (Intercept) -2.58289322 0.07711169 -33.4954800 5.608313e-246
## 2        mask_c  0.07250391 0.10333360   0.7016490  4.828981e-01
## 3        feat_c  0.35802662 0.10333820   3.4646106  5.309997e-04
## 4 mask_c:feat_c -0.19824320 0.20666460  -0.9592509  3.374324e-01

Third run

run3_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run3_data)
##            term     estimate  std.error    statistic       p.value
## 1   (Intercept) -2.620989644 0.07867521 -33.31404806 2.417198e-243
## 2        mask_c -0.004390499 0.08480725  -0.05177033  9.587117e-01
## 3        feat_c  0.295993160 0.08481014   3.49006801  4.828976e-04
## 4 mask_c:feat_c  0.019228611 0.16966914   0.11333004  9.097689e-01

Overall

feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                             family = binomial, data = question_first)
##            term    estimate  std.error   statistic      p.value
## 1   (Intercept) -2.53963192 0.04792108 -52.9961277 0.000000e+00
## 2        mask_c  0.05179057 0.05472818   0.9463236 3.439836e-01
## 3        feat_c  0.32306541 0.05473370   5.9024956 3.580437e-09
## 4 mask_c:feat_c  0.09328981 0.10946050   0.8522692 3.940647e-01